Optimization design of the bearingless switched reluctance motor based on SVM and GA

The optimization design method of the bearingless switched reluctance motor is presented. This paper mainly aims at nonlinear regression modeling of the bearingless switched reluctance motor with support vector machines, which is based on the finite element method simulating, and parameter optimization of the bearingless switched reluctance motor is based on genetic algorithms. The results prove that the nonparametric model has good precision. More important, the optimized motor can produce rated torque and has the maximum suspension force of every unit of rotor.